Mathematical model to reduce maternal and infant mortality in Southeast Asia

Harvard University
Cambridge, Massachusetts
Open Access
DOI: 10.18258/4103
Raised of $2,600 Goal
Funded on 12/31/14
Successfully Funded
  • $2,650
  • 101%
  • Funded
    on 12/31/14

About This Project

Maternal and child mortality in Southeast Asian countries is still very high, especially in poor and rural areas. The goal of our study is to develop user-friendly mathematical model to improve referral of high-risk term pregnancies in resource-poor settings; creating a more resource-efficient health system, while delivering better health outcomes to mothers and children.

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What is the context of this research?

The poor quality of antenatal, delivery and postnatal health care services is a major barrier to reducing maternal and child deaths. Across all population groups, the coverage relating to service quality, quantity, and access consistently score very low.

For example, of 2,100 obstetrician-gynaecologists (OB -GYN) in Indonesia, more than half practice in Java. This disparity causes limited quantity of human health resources in rural and underdeveloped areas.

With the relatively few resources and inadequate local capacities, knowledge of referral conditions should be strategically strengthened to promote appropriate use of these facilities.

What is the significance of this project?

Currently, our ability to define such a fetus with growth restriction (<2500 g) and macrosomia (>4000 g) for referral is limited. The most commonly used method is the Johnson-Toshach formula, especially in primary care setting where there’s no ultrasonography facility. However, various studies show that Johnson-Toshach is inaccurate. Effective screening of those with high-risk term pregnancies requires better precision for timely referral to the higher level facilities.

There is a need for a formula that does better than Johnson-Toshach in accurately predicting fetal weight on strategies to detect high risk pregnancies and accurate referral system while minimizing unnecessary referrals.

This will make healthcare delivery more efficient: saving lives and reducing costs.

What are the goals of the project?

Given this knowledge gap, we would like to develop an improved mathematical model, more accurate and easier to use, to better predict fetal weights at risk in Southeast Asia. This study sought to take into account the field’s constraints, such as births in primary care, or attended only by traditional birth attendants or family members.

The lack of resources and advanced facilities’ setting, make it even more important to be able to detect high risk pregnancies with accurate and efficient formula, which is fundamental for high quality antenatal care.


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The project will be conducted very efficiently by Dr. Christian Suharlim, a researcher at the Harvard Center for Health Decision Science; and Dr. Novita Liman, a practicing physician in the remote and underdeveloped area of Barru, South Sulawesi, Indonesia.

All funds raised will be allocated towards data collection and analysis incidentals.

Meet the Team

Christian Suharlim
Christian Suharlim
Novita Liman
Novita Liman

Team Bio

Christian Suharlim, MD, MPH is a postdoctoral research and teaching fellow at the Center for Health Decision Science. His research at the center include investigating cost-effectiveness of DOT-HAART for HIV patients in Lima, Peru. Additionally, since 2013 he has been working in collaboration with Universitas Andalas to implement MDR-TB diagnosis improvement in West Sumatera, Indonesia.

His current interests include improving public policy through the use of cost-effectiveness analysis and promoting information dissemination through the media; and improving access to good quality primary care for the socio-economically disadvantaged.

Prior to HSPH, Dr. Suharlim practiced medicine at a community health center in the rural area of Panguragan, Indonesia. He gained a deeper understanding of Indonesia’s healthcare system during his work as an officer in the Ministry of Health in 2012.

Additional Information

Photo courtesy of Charles Pieters

Project Backers

  • 17Backers
  • 101%Funded
  • $2,650Total Donations
  • $35.59Average Donation
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